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For: Lee AWC, Costa CM, Strocchi M, Rinaldi CA, Niederer SA. Computational Modeling for Cardiac Resynchronization Therapy. J Cardiovasc Transl Res 2018;11:92-108. [PMID: 29327314 DOI: 10.1007/s12265-017-9779-4] [Cited by in Crossref: 23] [Cited by in F6Publishing: 14] [Article Influence: 5.8] [Reference Citation Analysis]
Number Citing Articles
1 Fan L, Yao J, Wang L, Xu D, Tang D. Optimization of Left Ventricle Pace Maker Location Using Echo-Based Fluid-Structure Interaction Models. Front Physiol 2022;13:843421. [DOI: 10.3389/fphys.2022.843421] [Reference Citation Analysis]
2 Regazzoni F, Salvador M, Africa P, Fedele M, Dedè L, Quarteroni A. A cardiac electromechanical model coupled with a lumped-parameter model for closed-loop blood circulation. Journal of Computational Physics 2022. [DOI: 10.1016/j.jcp.2022.111083] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
3 Del Corso G, Verzicco R, Viola F. A fast computational model for the electrophysiology of the whole human heart. Journal of Computational Physics 2022. [DOI: 10.1016/j.jcp.2022.111084] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Hwang M, Uhm J, Park MC, Shim EB, Lee CJ, Oh J, Yu HT, Kim T, Joung B, Pak H, Kang S, Lee M. In silico screening method for non-responders to cardiac resynchronization therapy in patients with heart failure: a pilot study. Int J Arrhythm 2022;23. [DOI: 10.1186/s42444-021-00052-w] [Reference Citation Analysis]
5 Khamzin S, Dokuchaev A, Bazhutina A, Chumarnaya T, Zubarev S, Lyubimtseva T, Lebedeva V, Lebedev D, Gurev V, Solovyova O. Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data. Front Physiol 2021;12:753282. [PMID: 34970154 DOI: 10.3389/fphys.2021.753282] [Reference Citation Analysis]
6 Fan L, Choy JS, Raissi F, Kassab GS, Lee LC. Optimization of cardiac resynchronization therapy based on a cardiac electromechanics-perfusion computational model. Comput Biol Med 2021;:105050. [PMID: 34823858 DOI: 10.1016/j.compbiomed.2021.105050] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Sedova K, Repin K, Donin G, Dam PV, Kautzner J. Clinical Utility of Body Surface Potential Mapping in CRT Patients. Arrhythm Electrophysiol Rev 2021;10:113-9. [PMID: 34401184 DOI: 10.15420/aer.2021.14] [Reference Citation Analysis]
8 Pagani S, Dede' L, Manzoni A, Quarteroni A. Data integration for the numerical simulation of cardiac electrophysiology. Pacing Clin Electrophysiol 2021;44:726-36. [PMID: 33594761 DOI: 10.1111/pace.14198] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
9 Albatat M, Arevalo H, Bergsland J, Strøm V, Balasingham I, Odland HH. Optimal pacing sites in cardiac resynchronization by left ventricular activation front analysis. Comput Biol Med 2021;128:104159. [PMID: 33301952 DOI: 10.1016/j.compbiomed.2020.104159] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Nguyen TD, Kadri OE, Voronov RS. An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine. Front Bioeng Biotechnol 2020;8:529365. [PMID: 33102452 DOI: 10.3389/fbioe.2020.529365] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Fan L, Namani R, Choy JS, Kassab GS, Lee LC. Effects of Mechanical Dyssynchrony on Coronary Flow: Insights From a Computational Model of Coupled Coronary Perfusion With Systemic Circulation. Front Physiol 2020;11:915. [PMID: 32922304 DOI: 10.3389/fphys.2020.00915] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Strocchi M, Gsell MAF, Augustin CM, Razeghi O, Roney CH, Prassl AJ, Vigmond EJ, Behar JM, Gould JS, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium. J Biomech 2020;101:109645. [PMID: 32014305 DOI: 10.1016/j.jbiomech.2020.109645] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
13 Albatat M, Bergsland J, Arevalo H, Odland HH, Wall S, Sundnes J, Balasingham I. Multisite pacing and myocardial scars: a computational study. Comput Methods Biomech Biomed Engin 2020;23:248-60. [PMID: 31958019 DOI: 10.1080/10255842.2020.1711885] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
14 Albatat M, Bergsland J, Arevalo H, Odland HH, Bose P, Halvorsen PS, Balasingham I. Technological and Clinical Challenges in Lead Placement for Cardiac Rhythm Management Devices. Ann Biomed Eng 2020;48:26-46. [DOI: 10.1007/s10439-019-02376-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
15 Santiago A, Aguado-Sierra J, Zavala-Aké M, Doste-Beltran R, Gómez S, Arís R, Cajas JC, Casoni E, Vázquez M. Fully coupled fluid-electro-mechanical model of the human heart for supercomputers. Int J Numer Method Biomed Eng 2018;34:e3140. [PMID: 30117302 DOI: 10.1002/cnm.3140] [Cited by in Crossref: 30] [Cited by in F6Publishing: 21] [Article Influence: 10.0] [Reference Citation Analysis]
16 Okada JI, Washio T, Sugiura S, Hisada T. Clinical and pharmacological application of multiscale multiphysics heart simulator, UT-Heart. Korean J Physiol Pharmacol 2019;23:295-303. [PMID: 31496866 DOI: 10.4196/kjpp.2019.23.5.295] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
17 Syomin FA, Zberia MV, Tsaturyan AK. Multiscale simulation of the effects of atrioventricular block and valve diseases on heart performance. Int J Numer Method Biomed Eng 2019;35:e3216. [PMID: 31083764 DOI: 10.1002/cnm.3216] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
18 Carpio EF, Gomez JF, Sebastian R, Lopez-Perez A, Castellanos E, Almendral J, Ferrero JM, Trenor B. Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study. Front Physiol 2019;10:74. [PMID: 30804805 DOI: 10.3389/fphys.2019.00074] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Willemen E, Schreurs R, Huntjens PR, Strik M, Plank G, Vigmond E, Walmsley J, Vernooy K, Delhaas T, Prinzen FW, Lumens J. The Left and Right Ventricles Respond Differently to Variation of Pacing Delays in Cardiac Resynchronization Therapy: A Combined Experimental- Computational Approach. Front Physiol 2019;10:17. [PMID: 30774598 DOI: 10.3389/fphys.2019.00017] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
20 Cansız B, Sveric K, Ibrahim K, Strasser RH, Linke A, Kaliske M. Towards predictive computer simulations in cardiology: Finite element analysis of personalized heart models. Z Angew Math Mech 2018;98:2155-76. [DOI: 10.1002/zamm.201800055] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]